{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import sys\n",
    "from sklearn.preprocessing import normalize\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "xy = np.loadtxt('C:\\\\Users\\\\SANHA\\\\Desktop\\\\new_data.csv', delimiter=',', dtype=np.float32)\n",
    "x_data=xy[:,0:-1]\n",
    "x_data = normalize(x_data, axis=0, norm='max')\n",
    "xy[:,0:-1]=x_data\n",
    "y_data=xy[:,[-1]]\n",
    "m_data=xy[:499,:]\n",
    "r_data=xy[:,:]\n",
    "g_data=xy[:0,:]\n",
    "F_data=xy[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Inputs\n",
    "def model_inputs(real_dim, noise_dim):\n",
    "    inputs_real_ = tf.placeholder(tf.float32, shape=[None, real_dim], name='inputs_real')\n",
    "    inputs_z_ = tf.placeholder(tf.float32, shape=[None, noise_dim], name='inputs_z')\n",
    "    \n",
    "    return inputs_real_, inputs_z_\n",
    "\n",
    "def leaky_relu(x, alpha):\n",
    "    return tf.maximum(alpha * x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generator Network\n",
    "def model_generator(z_input, out_dim, n_units=128, reuse=False, alpha=0.01):\n",
    "    # used to reuse variables, name scope\n",
    "    with tf.variable_scope('generator', reuse=reuse):\n",
    "        hidden_layer = tf.layers.dense(z_input, n_units, activation=None)\n",
    "        hidden_layer = leaky_relu(hidden_layer, alpha)\n",
    "        \n",
    "        logits = tf.layers.dense(hidden_layer, out_dim, activation=None)\n",
    "        outputs = tf.nn.sigmoid(logits)\n",
    "        \n",
    "        return outputs, logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Discriminator Network\n",
    "def model_discriminator(input, n_units=128, reuse=False, alpha=0.1):\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        hidden_layer = tf.layers.dense(input, n_units, activation=tf.nn.relu)\n",
    "        #hidden_layer = leaky_relu(hidden_layer, alpha)\n",
    "        \n",
    "        logits = tf.layers.dense(hidden_layer, 1, activation=None)\n",
    "        outputs = tf.nn.sigmoid(logits)\n",
    "        \n",
    "        return outputs, logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameter\n",
    "input_size = 42\n",
    "z_dim = 21\n",
    "g_hidden_size = 128\n",
    "d_hidden_size = 128\n",
    "alpha = 0.1\n",
    "smooth = 0.1\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()  # If we don't have this, as we call this block over and over, the graph gets bigger and bigger\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    inputs_real, inputs_z = model_inputs(input_size, z_dim)\n",
    "    \n",
    "    g_outputs, g_logits = model_generator(inputs_z, input_size, n_units=g_hidden_size, reuse=False, alpha=alpha)\n",
    "    \n",
    "    d_outputs_real, d_logits_real = model_discriminator(inputs_real, n_units=d_hidden_size, reuse=False, alpha=alpha)\n",
    "    d_outputs_fake, d_logits_fake = model_discriminator(g_outputs, n_units=d_hidden_size, reuse=True, alpha=alpha)\n",
    "    \n",
    "    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1-smooth)))\n",
    "    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))\n",
    "    \n",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))\n",
    "    \n",
    "    t_vars = tf.trainable_variables()\n",
    "    g_vars = [variable for variable in t_vars if 'generator' in variable.name]\n",
    "    d_vars = [variable for variable in t_vars if 'discriminator' in variable.name]\n",
    "    \n",
    "    # Affected Variables with var_list\n",
    "    d_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(d_loss, var_list=d_vars)\n",
    "    g_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(g_loss, var_list=g_vars)\n",
    "    \n",
    "    # Saving variables with var_list\n",
    "    saver = tf.train.Saver(var_list=g_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'list_loss_g' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-ffac284b348b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_loss_g\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlist_loss_d\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'list_loss_g' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(list_loss_g,list_loss_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'list_loss_d' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-d60acab42a34>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlist_loss_d\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'list_loss_d' is not defined"
     ]
    }
   ],
   "source": [
    "list_loss_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_loss_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28488 / 100000: 0.697456, 1.169728"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-b753d1232b2a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     23\u001b[0m         \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0msample_z\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz_dim\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# 16 Samples each epoch\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m         \u001b[0mgen_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs_z\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreuse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0minputs_z\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0msample_z\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m         \u001b[0mtemp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgen_samples\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m41\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py35\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    898\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    899\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 900\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    901\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    902\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py35\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1134\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1135\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1136\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1137\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py35\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1314\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1315\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1316\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1317\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1318\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py35\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1320\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1321\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1322\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1323\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1324\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py35\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1303\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1304\u001b[0m       \u001b[1;31m# Ensure any changes to the graph are reflected in the runtime.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1305\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1306\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m   1307\u001b[0m           options, feed_dict, fetch_list, target_list, run_metadata)\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py35\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_extend_graph\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1338\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_created_with_new_api\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1339\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1340\u001b[1;33m         \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExtendSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1341\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1342\u001b[0m       \u001b[1;31m# Ensure any changes to the graph are reflected in the runtime.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "samples=[]\n",
    "normal=0\n",
    "abnormal=0\n",
    "list_loss_d=[]\n",
    "list_loss_g=[]\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    f = open('C:\\\\Users\\\\SANHA\\\\Desktop\\\\gen_sample100000.txt', 'a+')\n",
    "    for step in range(100000):\n",
    "        \n",
    "        \n",
    "        batch_images = FF_data[step].reshape([1, 42])\n",
    "        batch_z = np.random.uniform(-1, 1, size=[1, z_dim])\n",
    "        \n",
    "        _ = sess.run(d_optimizer, feed_dict={inputs_real : batch_images, inputs_z : batch_z})\n",
    "        _ = sess.run(g_optimizer, feed_dict={inputs_z : batch_z})\n",
    "        loss_d, loss_g = sess.run([d_loss, g_loss], feed_dict={inputs_real : batch_images, inputs_z : batch_z})\n",
    "        list_loss_d.append(loss_d)\n",
    "        list_loss_g.append(loss_g)\n",
    "        #if step%1000==0:\n",
    "        #    print('step {} / {} Complete. D_Loss : {:0.3f}, G_Loss : {:0.3f}'.format(step+1, 100000, loss_d, loss_g))\n",
    "        sys.stdout.write(\"\\r%d / %d: %f, %f\" % (step, 100000, loss_d, loss_g))\n",
    "        sys.stdout.flush()\n",
    "        sample_z = np.random.uniform(-1, 1, size=[1, z_dim])  # 16 Samples each epoch\n",
    "        gen_samples, _ = sess.run(model_generator(inputs_z, input_size, reuse=True), feed_dict={inputs_z : sample_z})\n",
    "        \n",
    "        temp=gen_samples[0,41]\n",
    "        #print(temp)\n",
    "        if temp>=0.5:\n",
    "            gen_samples[0,41]=1\n",
    "            abnormal+=1\n",
    "        else :\n",
    "            gen_samples[0,41]=0\n",
    "            normal+=1\n",
    "        #print(temp,gen_samples[0,41])\n",
    "        #write for text to csv\n",
    "        f.write(\"%f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f \\n\" %(gen_samples[0,0],gen_samples[0,1],gen_samples[0,2],gen_samples[0,3],gen_samples[0,4],gen_samples[0,5],gen_samples[0,6],gen_samples[0,7],gen_samples[0,8],gen_samples[0,9],gen_samples[0,10],gen_samples[0,11],gen_samples[0,12],gen_samples[0,13],gen_samples[0,14],gen_samples[0,15],gen_samples[0,16],gen_samples[0,17],gen_samples[0,18],gen_samples[0,19],gen_samples[0,20],gen_samples[0,21],gen_samples[0,22],gen_samples[0,23],gen_samples[0,24],gen_samples[0,25],gen_samples[0,26],gen_samples[0,27],gen_samples[0,28],gen_samples[0,29],gen_samples[0,30],gen_samples[0,31],gen_samples[0,32],gen_samples[0,33],gen_samples[0,34],gen_samples[0,35],gen_samples[0,36],gen_samples[0,37],gen_samples[0,38],gen_samples[0,39],gen_samples[0,40],gen_samples[0,41]))\n",
    "       \n",
    "        \n",
    "    print('Generating Complete. normal={}, abnormal={}'.format(normal,abnormal))\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished\n"
     ]
    }
   ],
   "source": [
    "xy2 = np.genfromtxt('C:\\\\Users\\\\SANHA\\\\Desktop\\\\mix_data_40000.csv', delimiter=',', dtype=np.float32)\n",
    "xy3 = np.loadtxt('C:\\\\Users\\\\SANHA\\\\Desktop\\\\gen_data.csv', delimiter=',', dtype=np.float32)\n",
    "xy4 = np.loadtxt('C:\\\\Users\\\\SANHA\\\\Desktop\\\\false_data.csv', delimiter=',', dtype=np.float32)\n",
    "#gen data 20000\n",
    "gx_data=xy3[:,0:-1]\n",
    "gy_data=xy3[:,[-1]]\n",
    "gF_data=xy3[:,:]\n",
    "\n",
    "#gen 20000+real 20000\n",
    "mx_data=xy2[:39999,0:-1]\n",
    "my_data=xy2[:39999,[-1]]\n",
    "mF_data=xy2[:39999,:]\n",
    "\n",
    "#real data 40000\n",
    "tx_data=r_data[:10000,0:-1]\n",
    "ty_data=r_data[:10000,[-1]]\n",
    "\n",
    "#false data\n",
    "FF_data=xy4[:,:]\n",
    "Fx_data=xy4[:,0:-1]\n",
    "Fy_data=xy4[:,[-1]]\n",
    "print(\"finished\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:     0\tLoss: 4.155\tAcc: 53.46%\n",
      "step:   100\tLoss: 0.125\tAcc: 95.52%\n",
      "step:   200\tLoss: 0.090\tAcc: 97.05%\n",
      "step:   300\tLoss: 0.069\tAcc: 97.56%\n",
      "step:   400\tLoss: 0.054\tAcc: 98.02%\n",
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true=19971 false: 29 acc: 1.00\n"
     ]
    }
   ],
   "source": [
    "#real data 40000\n",
    "tx_data=r_data[80000:100000,0:-1]\n",
    "ty_data=r_data[80000:100000,[-1]]\n",
    "\n",
    "nb_classes=2\n",
    "\n",
    "X=tf.placeholder(tf.float32,[None,41])\n",
    "Y=tf.placeholder(tf.int32,[None,1])\n",
    "\n",
    "Y_one_hot=tf.one_hot(Y,nb_classes)\n",
    "Y_one_hot=tf.reshape(Y_one_hot,[-1,nb_classes])\n",
    "\n",
    "W1=tf.Variable(tf.random_normal([41,41]),name='weight1')\n",
    "b1=tf.Variable(tf.random_normal([41]),name='bias1')\n",
    "layer1=tf.sigmoid(tf.matmul(X,W1)+b1)\n",
    "\n",
    "W2=tf.Variable(tf.random_normal([41,41]),name='weight2')\n",
    "b2=tf.Variable(tf.random_normal([41]),name='bias2')\n",
    "layer2=tf.sigmoid(tf.matmul(layer1,W2)+b2)\n",
    "\n",
    "W3=tf.Variable(tf.random_normal([41,nb_classes]),name='weight3')\n",
    "b3=tf.Variable(tf.random_normal([nb_classes]),name='bias3')\n",
    "logits=tf.matmul(layer2,W3)+b3\n",
    "hypothesis=tf.nn.softmax(logits)\n",
    "\n",
    "cost_i=tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y_one_hot)\n",
    "\n",
    "cost=tf.reduce_mean(cost_i)\n",
    "optimizer=tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)\n",
    "\n",
    "prediction=tf.argmax(hypothesis,1) #가능성을 퍼센트로~~\n",
    "correct_prediction=tf.equal(prediction,tf.arg_max(Y_one_hot,1))\n",
    "accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "\n",
    "#input_F_data=input_F_data\n",
    "input_train_x_data=x_data\n",
    "input_train_y_data=y_data\n",
    "\n",
    "input_test_x_data=x_data\n",
    "input_test_y_data=y_data\n",
    "\n",
    "output_x_data=tx_data\n",
    "output_y_data=ty_data\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    #f = open('C:\\\\Users\\\\SANHA\\\\Desktop\\\\false_data.txt', 'w')\n",
    "    for step in range(10000):\n",
    "        sess.run(optimizer,feed_dict={X:input_train_x_data,Y:input_train_y_data})\n",
    "        #print(\"training by gan sample\")\n",
    "        if step %100==0:\n",
    "            loss,acc=sess.run([cost,accuracy],feed_dict={X:input_test_x_data,Y:input_test_y_data})\n",
    "            print(\"step: {:5}\\tLoss: {:.3f}\\tAcc: {:.2%}\".format(step,loss,acc))\n",
    "\n",
    "\n",
    "#m_data=np.append(m_data,gen_samples,axis=0)\n",
    "\n",
    "    pred = sess.run(prediction, feed_dict={X: output_x_data})\n",
    "    tr=0\n",
    "    fa=0\n",
    "    total=0\n",
    "    fig = plt.figure(figsize=(15,9))\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    ax.set(xlim=[0, 42], ylim=[0,1.5])\n",
    "    for p, y in zip(pred,output_y_data.flatten()):\n",
    "            if(total%1000==0):\n",
    "                print(total)\n",
    "            if(p==int(y)): \n",
    "                tr+=1\n",
    "                total+=1\n",
    "                \n",
    "                #if(int(y)==1):\n",
    "                line, = ax.plot([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42], F_data[total,:].flatten())\n",
    "                line.set(color='0.75',\n",
    "                 linewidth=1, linestyle='',\n",
    "                 marker='o', markersize=1, markeredgewidth=1,\n",
    "                 markerfacecolor='0.75', markeredgecolor='0.75')\n",
    "            else:\n",
    "                fa+=1\n",
    "                total+=1\n",
    "                if(int(y)==1): #오탐했고 abnormal할때\n",
    "                    line, = ax.plot([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42], F_data[total,:].flatten())\n",
    "                    line.set(color='red',\n",
    "                     linewidth=1, linestyle='--',\n",
    "                     marker='o', markersize=2, markeredgewidth=1,\n",
    "                     markerfacecolor='red', markeredgecolor='red')\n",
    "                if(int(y)==0): #오탐했고 normal할때\n",
    "                    line, = ax.plot([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42], F_data[total,:].flatten())\n",
    "                    line.set(color='green',\n",
    "                    linewidth=1, linestyle='--',\n",
    "                    marker='o', markersize=2, markeredgewidth=1,\n",
    "                    markerfacecolor='green', markeredgecolor='green')\n",
    "                \n",
    "    line.set(color='green', label='true')\n",
    "    line.set(color='red', label='false')\n",
    "    plt.legend()\n",
    "    plt.show()                          \n",
    "   \n",
    "    #f.close()\n",
    "    print(\"true={} false: {} acc: {:0.2f}\".format(tr,fa,tr/(tr+fa)))"
   ]
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